🔍🛠️ Tavily Search & Extract - Template

This workflow integrates artificial intelligence with a weather API to provide an intelligent weather inquiry service. Users only need to enter the city name and the number of days for the query in the chat interface, and the system will automatically retrieve the city's latitude and longitude, as well as future weather information, offering accurate weather forecasts. It supports multi-turn dialogue memory, enhancing the user experience. This service is suitable for scenarios such as travel planning, education and training, and intelligent customer service, allowing users to quickly obtain the weather data they need, thereby assisting with daily travel and decision-making.

This workflow achieves intelligent management and querying of bank statements by monitoring the addition, deletion, and modification events within a local folder. It utilizes a vector database to synchronize file content, generating efficient semantic vector embeddings that support natural language interaction, thereby enhancing query accuracy and response speed. Users can quickly locate and understand a large number of financial documents, significantly improving the utilization efficiency of financial data and the querying experience.

This workflow automatically fetches RSS news from multiple well-known technology websites, utilizing AI technology for intelligent analysis and summarization of the content. It generates a personalized weekly technology news briefing and sends it to users via email. Through this automated process, users can efficiently filter key information, avoid information overload, and easily stay updated on industry trends. It is suitable for tech enthusiasts, corporate teams, and professionals, enhancing information retrieval efficiency and reading experience.

This workflow primarily implements the automatic crawling of the latest articles from Paul Graham's official website, extracting and vectorizing the content to store it in the Milvus database. Users can quickly query relevant information through an intelligent Q&A system. By leveraging OpenAI's text generation capabilities, the system can provide users with precise answers, significantly enhancing the efficiency and accuracy of information retrieval. It is suitable for various scenarios, including academic research, knowledge base construction, and educational training.

This workflow builds an intelligent chatbot that utilizes retrieval-augmented generation technology to extract information from Google Drive documents, combined with natural language processing for smart Q&A. It supports batch downloading of documents, metadata extraction, and text vectorization storage, enabling efficient semantic search. Operations notifications and manual reviews are implemented through Telegram to ensure data security, making it suitable for scenarios such as enterprise knowledge bases, legal consulting, and customer support, thereby enhancing information retrieval and human-computer interaction efficiency.

This workflow automatically downloads eBooks from Google Drive, splits the text, and generates vectors, which are stored in the Supabase vector database. Users can ask questions in real-time through a chat interface, and the system quickly provides intelligent answers using vector retrieval and question-answering chain technology. Additionally, it supports operations for adding, deleting, modifying, and querying documents, enhancing the flexibility of knowledge base management. This makes it suitable for enterprise knowledge management, educational tutoring, and content extraction needs in research institutions.

The API architecture crawling and extraction workflow is an intelligent automation tool that efficiently searches, crawls, and extracts API documentation for specified services. By integrating search engines, web crawlers, and large language models, this workflow not only accurately identifies API operations but also structures the information for storage in Google Sheets. Additionally, it generates customized API architecture JSON files for centralized management and sharing, significantly enhancing development and integration efficiency, and helping users quickly obtain and organize API information.

This workflow automates the process of web data scraping, extracting and formatting content, generating high-quality text vector embeddings, and storing them in a vector database, forming a complete data processing loop. By combining efficient data crawling, intelligent content extraction, and vector retrieval technologies, users can quickly build vector datasets suitable for training large language models, enhancing data quality and processing efficiency, and making it applicable to various scenarios such as machine learning, intelligent search, and knowledge management.